Isolated fixes
Rules that fix one problem create two more. Escalating a priority floods an already-congested zone.
Decision Intelligence Layer
OPTIMIZE THE SHIFT,
NOT JUST THE PICK.
Tessera sits on top of your WMS and decides what work to release, how to group it, and what to prioritize — accounting for every constraint on the floor simultaneously.
The Problem
VISIBILITY ISN'T THE GAP. THE RESPONSE IS.
Your WMS can see the problem. It cannot tell you what to do about it given everything else happening on the floor.
Frozen configuration
WMS settings reflect last year's reality. Nobody re-tunes because the interdependencies are too complex.
No way to compare
You can see what happened, but not what a better decision would have produced.
Exception paralysis
Every disruption - short picks, late trailers, hot orders - requires a judgment call with incomplete information.
EXPLAIN, DECIDE, PREDICT, EMPOWER.
One model. Four capabilities. Every decision grounded in the same optimization that reasons about all constraints simultaneously.
Step 01
EXPLAIN
Trace why the floor looks the way it does. Not alerts — causal reasoning. This zone is congested because the last release pushed too many orders into a narrow area, and batching is concentrating picks there.
Step 02
DECIDE
Prescribe a fix that accounts for every constraint at once — deadlines, congestion, travel, labor, zone capacity. One optimization pass, not one rule at a time.
Step 03
PREDICT
See impact before you commit. The same model that decides also predicts — what happens at 80 orders instead of 120? Zero gap between forecast and action.
Step 04
EMPOWER
Surface the trade-off space so operators set strategic intent rather than react to individual alerts. Shift-level posture, on-demand alternatives, and Tess's Choice — your intuition guides the system, the optimizer handles the rest.
TALK TO YOUR OPTIMIZER. TALK TO TESS.
Tess doesn't query a dashboard. It invokes the optimizer — modifying inputs, running scenarios, and tracing every answer to a specific constraint or metric in the model.
Operator: We have a 2pm carrier cutoff and we're short-staffed in Zone B.
Tess: Updated posture. Deadline compliance weight increased to 0.6 and Zone B capped at 35% active work. Next optimization cycle will reflect both changes.
Operator: Why is Zone C slowing down?
Tess: The last release pushed 38 tasks into Zone C against a capacity of 40. Batching is concentrating picks in aisles C3–C5. I've throttled the next release to 12 tasks for that zone and redistributed 8 tasks to Zone D.
Operator: How do I get to zero late-risk orders this cycle?
Tess: I ran the optimizer with deadline compliance at maximum weight. Zero late-risk is achievable — it requires raising the Zone A task cap from 35 to 48 and accepting 22% more total travel. Three batches currently routed through Zone D would need to reroute through Zone A to meet their cutoffs. Alternative: add 2 carts and I can get to zero late-risk with only 9% more travel and no cap change.
RIGOR YOU CAN MEASURE.
-12%
Travel distance
-8%
Pick time
+15%
Throughput at same labor
100%
Constraint compliance
Placeholder metrics. Replace with observed facility data when available.
READY TO SEE IT RUN?
Bring one facility. We'll produce an executable plan.